本文選題：礦井水源　切入點：KNN　出處：《安徽理工大學》2017年碩士論文

【摘要】：在煤礦井下,發(fā)生的水害水災是礦井安全工作中的重點防治對象。突水是水災主要的體現,一旦發(fā)生,則會造成嚴重的人身和經濟損失。所以,防治水害的工作是非常重要的。在水害防治工作中,對于礦井水源的識別工作也是必不可少的,對于傳統(tǒng)的識別方法,如水化學方法,其耗時長、效率低等缺點都沒能很好地解決。針對這些情況,本文提出了利用KNN算法結合LIF技術在礦井水源識別的應用。首先分析煤礦井下水源的由來,詳細介紹其產生的原因與現階段礦井水源所處的地下層,分析對礦井安全的危害。然后對礦井水源的水樣提取做出了要求和介紹,對于礦井下水樣的提取工作,是非常困難的,而且所提取的水樣需要進行實驗前處理,達到實驗所需的要求。再對實驗所用的實驗設備進行了介紹,實驗的設備是自主研制的礦用設備,目前處于實驗室階段。利用該設備,對所采集到的礦井水源進行光譜數據的采集,設置好設備參數,保證采集過程在暗室進行,之后將采集的光譜原始數據存儲在上位機中,待用。在光譜數據處理之前,需要對其進行光譜預處理,本文采用多種預處理方式,起到對比的作用,在其中選取最佳的光譜預處理方法。本文還介紹了 KNN算法以及一些改進的KNN算法,對于改進的算法進行了原理分析。并在實驗中進行多種改進的KNN算法同時對光譜數據進行處理分類,在改變K值的基礎上,對多種改進KNN算法的準確度進行分析,選取最佳的KNN算法。實驗所用到的軟件有MATLAB和SPSS,對數據處理有很大的功能,操作起來也非常簡單。最后,對來自淮南某一礦區(qū)所采集的礦井水源進行了實際的分類實驗,利用改進的KNN算法對光譜數據進行分類,所分類的準確度非�？捎^,再次證明了 KNN算法在礦井水源識別中的應用是非�？尚械�,而且具有很高的使用價值。對于KNN算法在礦井水源中的應用,本文所提出的這種識別分類方法是第一次應用。對于其仿真結果和實際的實驗分析結果來說,都說明了,KNN算法在礦井水源識別的應用中是非常值得研究的。也充分展示了,LIF技術在此領域的特殊之處,能夠快速的建立模型對未知的水樣進行識別分類。這對于今后的煤礦產業(yè)安全工作,起到了里程碑性的進步。
[Abstract]:In the coal mine underground, the water disaster flood is the key prevention object in the mine safety work. Water inrush is the main embodiment of the flood. Once it occurs, it will cause serious personal and economic losses. The prevention and control of water hazards is very important. In the prevention and control of water hazards, it is also necessary for the identification of mine water sources, and for traditional identification methods, such as hydrochemical methods, it takes a long time. The shortcomings of low efficiency have not been solved well. In view of these conditions, this paper puts forward the application of KNN algorithm combined with LIF technology in mine water source identification. Firstly, the origin of underground water source in coal mine is analyzed. This paper introduces the causes of mine water source and the underground layer of mine water source at this stage, analyzes the harm to mine safety, and then makes a request and introduction to the water sample extraction of mine water source, which is very difficult to extract mine water sample. Moreover, the extracted water samples need to be treated before the experiment to meet the requirements of the experiment. Then the experimental equipment used in the experiment is introduced. The experimental equipment is a self-developed mine equipment, which is currently in the laboratory stage. To collect the spectral data of mine water source, set the parameters of the equipment to ensure that the collection process is carried out in the dark room, and then store the original spectral data in the upper computer to be used. Before the spectral data processing, In this paper, we choose the best spectral pretreatment method, and we also introduce the KNN algorithm and some improved KNN algorithm. The principle of the improved algorithm is analyzed, and several improved KNN algorithms are used to process and classify the spectral data in the experiment. On the basis of changing the K value, the accuracy of the improved KNN algorithm is analyzed. The best KNN algorithm is selected. The software used in the experiment is MATLAB and SPSS, which has great function in data processing and is very simple to operate. Finally, the actual classification experiment of mine water collected from a mining area in Huainan is carried out. Using the improved KNN algorithm to classify the spectral data, the accuracy of the classification is very considerable. It is proved that the application of the KNN algorithm in mine water source recognition is very feasible. For the application of KNN algorithm in mine water source, the method proposed in this paper is the first application. It shows that the application of KNN algorithm in mine water source identification is very worthy of study, and fully demonstrates the special features of LIF technology in this field. It can quickly establish the model to identify and classify the unknown water samples, which is a milestone progress for the future safety work of coal mine industry.
【學位授予單位】：安徽理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TD745.2

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